A Peer-Based Approach on Analyzing Hacked Twitter Accounts

dc.contributor.author Murauer, Benjamin
dc.contributor.author Zangerle, Eva
dc.contributor.author Specht, Günther
dc.date.accessioned 2016-12-29T00:45:43Z
dc.date.available 2016-12-29T00:45:43Z
dc.date.issued 2017-01-04
dc.description.abstract Social media has become an important part of the lives of their hundreds of millions of users. Hackers make use of the large target audience by sending malicious content, often by hijacking existing accounts. This phenomenon has caused widespread research on how to detect hacked accounts, where different approaches exist. This work sets out to analyze the possibilities of including the reactions of hacked Twitter accounts’ peers into a detection system. Based on a dataset of six million tweets crawled from Twitter over the course of two years, we select a subset of tweets in which users react to alleged hacks of other accounts. We then gather and analyze the responses to those messages to reconstruct the conversations made. A quantitative analysis of these conversations shows that 30% of the users that are allegedly being hacked reply to the accusations, suggesting that these users acknowledge that their account was hacked.
dc.format.extent 10 pages
dc.identifier.doi 10.24251/HICSS.2017.224
dc.identifier.isbn 978-0-9981331-0-2
dc.identifier.uri http://hdl.handle.net/10125/41378
dc.language.iso eng
dc.relation.ispartof Proceedings of the 50th Hawaii International Conference on System Sciences
dc.rights Attribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.uri https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject hacked account detection
dc.subject social media analysis
dc.subject Twitter
dc.title A Peer-Based Approach on Analyzing Hacked Twitter Accounts
dc.type Conference Paper
dc.type.dcmi Text
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